Overview

Dataset statistics

Number of variables24
Number of observations129880
Missing cells393
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.8 MiB
Average record size in memory192.0 B

Variable types

Numeric18
Categorical6

Alerts

Inflight wifi service is highly overall correlated with Ease of Online booking and 1 other fieldsHigh correlation
Ease of Online booking is highly overall correlated with Inflight wifi serviceHigh correlation
Food and drink is highly overall correlated with Seat comfort and 2 other fieldsHigh correlation
Online boarding is highly overall correlated with SatisfactionHigh correlation
Seat comfort is highly overall correlated with Food and drink and 2 other fieldsHigh correlation
Inflight entertainment is highly overall correlated with Food and drink and 2 other fieldsHigh correlation
On-board service is highly overall correlated with Inflight serviceHigh correlation
Inflight service is highly overall correlated with On-board serviceHigh correlation
Cleanliness is highly overall correlated with Food and drink and 2 other fieldsHigh correlation
Departure Delay in Minutes is highly overall correlated with Arrival Delay in MinutesHigh correlation
Arrival Delay in Minutes is highly overall correlated with Departure Delay in MinutesHigh correlation
Type of Travel is highly overall correlated with ClassHigh correlation
Class is highly overall correlated with Type of Travel and 1 other fieldsHigh correlation
Satisfaction is highly overall correlated with Inflight wifi service and 2 other fieldsHigh correlation
id is uniformly distributedUniform
id has unique valuesUnique
Inflight wifi service has 3916 (3.0%) zerosZeros
Departure/Arrival time convenient has 6681 (5.1%) zerosZeros
Ease of Online booking has 5682 (4.4%) zerosZeros
Online boarding has 3080 (2.4%) zerosZeros
Departure Delay in Minutes has 73356 (56.5%) zerosZeros
Arrival Delay in Minutes has 72753 (56.0%) zerosZeros

Reproduction

Analysis started2023-10-31 12:23:32.534968
Analysis finished2023-10-31 12:24:12.013399
Duration39.48 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct129880
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64940.5
Minimum1
Maximum129880
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-10-31T13:24:12.071775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6494.95
Q132470.75
median64940.5
Q397410.25
95-th percentile123386.05
Maximum129880
Range129879
Interquartile range (IQR)64939.5

Descriptive statistics

Standard deviation37493.271
Coefficient of variation (CV)0.57734805
Kurtosis-1.2
Mean64940.5
Median Absolute Deviation (MAD)32470
Skewness1.0956429 × 10-18
Sum8.4344721 × 109
Variance1.4057454 × 109
MonotonicityNot monotonic
2023-10-31T13:24:12.180668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70172 1
 
< 0.1%
105847 1
 
< 0.1%
127259 1
 
< 0.1%
53818 1
 
< 0.1%
56307 1
 
< 0.1%
55206 1
 
< 0.1%
105921 1
 
< 0.1%
48518 1
 
< 0.1%
97504 1
 
< 0.1%
54225 1
 
< 0.1%
Other values (129870) 129870
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
129880 1
< 0.1%
129879 1
< 0.1%
129878 1
< 0.1%
129877 1
< 0.1%
129876 1
< 0.1%
129875 1
< 0.1%
129874 1
< 0.1%
129873 1
< 0.1%
129872 1
< 0.1%
129871 1
< 0.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1014.8 KiB
Female
65899 
Male
63981 

Length

Max length6
Median length6
Mean length5.0147675
Min length4

Characters and Unicode

Total characters651318
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 65899
50.7%
Male 63981
49.3%

Length

2023-10-31T13:24:12.283835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T13:24:12.387945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
female 65899
50.7%
male 63981
49.3%

Most occurring characters

ValueCountFrequency (%)
e 195779
30.1%
a 129880
19.9%
l 129880
19.9%
F 65899
 
10.1%
m 65899
 
10.1%
M 63981
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 521438
80.1%
Uppercase Letter 129880
 
19.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 195779
37.5%
a 129880
24.9%
l 129880
24.9%
m 65899
 
12.6%
Uppercase Letter
ValueCountFrequency (%)
F 65899
50.7%
M 63981
49.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 651318
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 195779
30.1%
a 129880
19.9%
l 129880
19.9%
F 65899
 
10.1%
m 65899
 
10.1%
M 63981
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 651318
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 195779
30.1%
a 129880
19.9%
l 129880
19.9%
F 65899
 
10.1%
m 65899
 
10.1%
M 63981
 
9.8%

Customer Type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1014.8 KiB
Loyal Customer
106100 
disloyal Customer
23780 

Length

Max length17
Median length14
Mean length14.549276
Min length14

Characters and Unicode

Total characters1889660
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLoyal Customer
2nd rowdisloyal Customer
3rd rowLoyal Customer
4th rowLoyal Customer
5th rowLoyal Customer

Common Values

ValueCountFrequency (%)
Loyal Customer 106100
81.7%
disloyal Customer 23780
 
18.3%

Length

2023-10-31T13:24:12.465082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T13:24:12.555484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
customer 129880
50.0%
loyal 106100
40.8%
disloyal 23780
 
9.2%

Most occurring characters

ValueCountFrequency (%)
o 259760
13.7%
l 153660
 
8.1%
s 153660
 
8.1%
y 129880
 
6.9%
a 129880
 
6.9%
129880
 
6.9%
C 129880
 
6.9%
u 129880
 
6.9%
t 129880
 
6.9%
m 129880
 
6.9%
Other values (5) 413420
21.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1523800
80.6%
Uppercase Letter 235980
 
12.5%
Space Separator 129880
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 259760
17.0%
l 153660
10.1%
s 153660
10.1%
y 129880
8.5%
a 129880
8.5%
u 129880
8.5%
t 129880
8.5%
m 129880
8.5%
e 129880
8.5%
r 129880
8.5%
Other values (2) 47560
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
C 129880
55.0%
L 106100
45.0%
Space Separator
ValueCountFrequency (%)
129880
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1759780
93.1%
Common 129880
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 259760
14.8%
l 153660
8.7%
s 153660
8.7%
y 129880
7.4%
a 129880
7.4%
C 129880
7.4%
u 129880
7.4%
t 129880
7.4%
m 129880
7.4%
e 129880
7.4%
Other values (4) 283540
16.1%
Common
ValueCountFrequency (%)
129880
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1889660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 259760
13.7%
l 153660
 
8.1%
s 153660
 
8.1%
y 129880
 
6.9%
a 129880
 
6.9%
129880
 
6.9%
C 129880
 
6.9%
u 129880
 
6.9%
t 129880
 
6.9%
m 129880
 
6.9%
Other values (5) 413420
21.9%

Age
Real number (ℝ)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.427957
Minimum7
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-10-31T13:24:12.643914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile15
Q127
median40
Q351
95-th percentile64
Maximum85
Range78
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.11936
Coefficient of variation (CV)0.38346801
Kurtosis-0.71914023
Mean39.427957
Median Absolute Deviation (MAD)12
Skewness-0.0036062117
Sum5120903
Variance228.59505
MonotonicityNot monotonic
2023-10-31T13:24:12.747559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 3692
 
2.8%
25 3511
 
2.7%
40 3209
 
2.5%
44 3104
 
2.4%
41 3089
 
2.4%
42 3017
 
2.3%
43 2941
 
2.3%
45 2939
 
2.3%
23 2935
 
2.3%
22 2931
 
2.3%
Other values (65) 98512
75.8%
ValueCountFrequency (%)
7 685
0.5%
8 797
0.6%
9 859
0.7%
10 822
0.6%
11 837
0.6%
12 794
0.6%
13 806
0.6%
14 860
0.7%
15 1006
0.8%
16 1156
0.9%
ValueCountFrequency (%)
85 25
 
< 0.1%
80 110
0.1%
79 52
 
< 0.1%
78 44
 
< 0.1%
77 106
0.1%
76 60
 
< 0.1%
75 76
 
0.1%
74 61
 
< 0.1%
73 67
 
0.1%
72 249
0.2%

Type of Travel
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1014.8 KiB
Business travel
89693 
Personal Travel
40187 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters1948200
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPersonal Travel
2nd rowBusiness travel
3rd rowBusiness travel
4th rowBusiness travel
5th rowBusiness travel

Common Values

ValueCountFrequency (%)
Business travel 89693
69.1%
Personal Travel 40187
30.9%

Length

2023-10-31T13:24:12.848547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T13:24:12.936262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
travel 129880
50.0%
business 89693
34.5%
personal 40187
 
15.5%

Most occurring characters

ValueCountFrequency (%)
s 309266
15.9%
e 259760
13.3%
r 170067
8.7%
a 170067
8.7%
l 170067
8.7%
n 129880
6.7%
129880
6.7%
v 129880
6.7%
B 89693
 
4.6%
u 89693
 
4.6%
Other values (5) 299947
15.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1648253
84.6%
Uppercase Letter 170067
 
8.7%
Space Separator 129880
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 309266
18.8%
e 259760
15.8%
r 170067
10.3%
a 170067
10.3%
l 170067
10.3%
n 129880
7.9%
v 129880
7.9%
u 89693
 
5.4%
i 89693
 
5.4%
t 89693
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
B 89693
52.7%
P 40187
23.6%
T 40187
23.6%
Space Separator
ValueCountFrequency (%)
129880
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1818320
93.3%
Common 129880
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 309266
17.0%
e 259760
14.3%
r 170067
9.4%
a 170067
9.4%
l 170067
9.4%
n 129880
7.1%
v 129880
7.1%
B 89693
 
4.9%
u 89693
 
4.9%
i 89693
 
4.9%
Other values (4) 210254
11.6%
Common
ValueCountFrequency (%)
129880
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1948200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 309266
15.9%
e 259760
13.3%
r 170067
8.7%
a 170067
8.7%
l 170067
8.7%
n 129880
6.7%
129880
6.7%
v 129880
6.7%
B 89693
 
4.6%
u 89693
 
4.6%
Other values (5) 299947
15.4%

Class
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1014.8 KiB
Business
62160 
Eco
58309 
Eco Plus
9411 

Length

Max length8
Median length8
Mean length5.7552741
Min length3

Characters and Unicode

Total characters747495
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEco Plus
2nd rowBusiness
3rd rowBusiness
4th rowBusiness
5th rowBusiness

Common Values

ValueCountFrequency (%)
Business 62160
47.9%
Eco 58309
44.9%
Eco Plus 9411
 
7.2%

Length

2023-10-31T13:24:13.013773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T13:24:13.107403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
eco 67720
48.6%
business 62160
44.6%
plus 9411
 
6.8%

Most occurring characters

ValueCountFrequency (%)
s 195891
26.2%
u 71571
 
9.6%
E 67720
 
9.1%
c 67720
 
9.1%
o 67720
 
9.1%
B 62160
 
8.3%
i 62160
 
8.3%
n 62160
 
8.3%
e 62160
 
8.3%
9411
 
1.3%
Other values (2) 18822
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 598793
80.1%
Uppercase Letter 139291
 
18.6%
Space Separator 9411
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 195891
32.7%
u 71571
 
12.0%
c 67720
 
11.3%
o 67720
 
11.3%
i 62160
 
10.4%
n 62160
 
10.4%
e 62160
 
10.4%
l 9411
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
E 67720
48.6%
B 62160
44.6%
P 9411
 
6.8%
Space Separator
ValueCountFrequency (%)
9411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 738084
98.7%
Common 9411
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 195891
26.5%
u 71571
 
9.7%
E 67720
 
9.2%
c 67720
 
9.2%
o 67720
 
9.2%
B 62160
 
8.4%
i 62160
 
8.4%
n 62160
 
8.4%
e 62160
 
8.4%
P 9411
 
1.3%
Common
ValueCountFrequency (%)
9411
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 747495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 195891
26.2%
u 71571
 
9.6%
E 67720
 
9.1%
c 67720
 
9.1%
o 67720
 
9.1%
B 62160
 
8.3%
i 62160
 
8.3%
n 62160
 
8.3%
e 62160
 
8.3%
9411
 
1.3%
Other values (2) 18822
 
2.5%

Flight Distance
Real number (ℝ)

Distinct3821
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1190.3164
Minimum31
Maximum4983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-10-31T13:24:13.193919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile176.95
Q1414
median844
Q31744
95-th percentile3380
Maximum4983
Range4952
Interquartile range (IQR)1330

Descriptive statistics

Standard deviation997.45248
Coefficient of variation (CV)0.83797256
Kurtosis0.2655029
Mean1190.3164
Median Absolute Deviation (MAD)518
Skewness1.1081423
Sum1.5459829 × 108
Variance994911.44
MonotonicityNot monotonic
2023-10-31T13:24:13.298731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
337 841
 
0.6%
594 505
 
0.4%
404 480
 
0.4%
862 473
 
0.4%
2475 470
 
0.4%
447 457
 
0.4%
236 438
 
0.3%
192 424
 
0.3%
308 402
 
0.3%
214 398
 
0.3%
Other values (3811) 124992
96.2%
ValueCountFrequency (%)
31 11
 
< 0.1%
56 11
 
< 0.1%
67 160
0.1%
73 77
0.1%
74 42
 
< 0.1%
76 2
 
< 0.1%
77 57
 
< 0.1%
78 37
 
< 0.1%
80 3
 
< 0.1%
82 11
 
< 0.1%
ValueCountFrequency (%)
4983 16
< 0.1%
4963 19
< 0.1%
4817 6
 
< 0.1%
4502 14
< 0.1%
4243 23
< 0.1%
4000 12
< 0.1%
3999 5
 
< 0.1%
3998 12
< 0.1%
3997 12
< 0.1%
3996 11
< 0.1%

Inflight wifi service
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7286957
Minimum0
Maximum5
Zeros3916
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-10-31T13:24:13.379961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3293401
Coefficient of variation (CV)0.48717053
Kurtosis-0.8485981
Mean2.7286957
Median Absolute Deviation (MAD)1
Skewness0.040465252
Sum354403
Variance1.7671452
MonotonicityNot monotonic
2023-10-31T13:24:13.452542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 32320
24.9%
3 32185
24.8%
4 24775
19.1%
1 22328
17.2%
5 14356
11.1%
0 3916
 
3.0%
ValueCountFrequency (%)
0 3916
 
3.0%
1 22328
17.2%
2 32320
24.9%
3 32185
24.8%
4 24775
19.1%
5 14356
11.1%
ValueCountFrequency (%)
5 14356
11.1%
4 24775
19.1%
3 32185
24.8%
2 32320
24.9%
1 22328
17.2%
0 3916
 
3.0%

Departure/Arrival time convenient
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0575993
Minimum0
Maximum5
Zeros6681
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-10-31T13:24:13.524261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5267415
Coefficient of variation (CV)0.49932687
Kurtosis-1.0408962
Mean3.0575993
Median Absolute Deviation (MAD)1
Skewness-0.33246886
Sum397121
Variance2.3309396
MonotonicityNot monotonic
2023-10-31T13:24:13.599998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 31880
24.5%
5 27998
21.6%
3 22378
17.2%
2 21534
16.6%
1 19409
14.9%
0 6681
 
5.1%
ValueCountFrequency (%)
0 6681
 
5.1%
1 19409
14.9%
2 21534
16.6%
3 22378
17.2%
4 31880
24.5%
5 27998
21.6%
ValueCountFrequency (%)
5 27998
21.6%
4 31880
24.5%
3 22378
17.2%
2 21534
16.6%
1 19409
14.9%
0 6681
 
5.1%

Ease of Online booking
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7568756
Minimum0
Maximum5
Zeros5682
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-10-31T13:24:13.672650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4017396
Coefficient of variation (CV)0.50845226
Kurtosis-0.91352348
Mean2.7568756
Median Absolute Deviation (MAD)1
Skewness-0.018778932
Sum358063
Variance1.9648739
MonotonicityNot monotonic
2023-10-31T13:24:13.747849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 30393
23.4%
2 30051
23.1%
4 24444
18.8%
1 21886
16.9%
5 17424
13.4%
0 5682
 
4.4%
ValueCountFrequency (%)
0 5682
 
4.4%
1 21886
16.9%
2 30051
23.1%
3 30393
23.4%
4 24444
18.8%
5 17424
13.4%
ValueCountFrequency (%)
5 17424
13.4%
4 24444
18.8%
3 30393
23.4%
2 30051
23.1%
1 21886
16.9%
0 5682
 
4.4%

Gate location
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9769249
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-10-31T13:24:13.819548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2785197
Coefficient of variation (CV)0.42947664
Kurtosis-1.0315534
Mean2.9769249
Median Absolute Deviation (MAD)1
Skewness-0.058264886
Sum386643
Variance1.6346126
MonotonicityNot monotonic
2023-10-31T13:24:13.895103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 35717
27.5%
4 30466
23.5%
2 24296
18.7%
1 21991
16.9%
5 17409
13.4%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 21991
16.9%
2 24296
18.7%
3 35717
27.5%
4 30466
23.5%
5 17409
13.4%
ValueCountFrequency (%)
5 17409
13.4%
4 30466
23.5%
3 35717
27.5%
2 24296
18.7%
1 21991
16.9%
0 1
 
< 0.1%

Food and drink
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2047736
Minimum0
Maximum5
Zeros132
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-10-31T13:24:13.966799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3299331
Coefficient of variation (CV)0.41498503
Kurtosis-1.1453811
Mean3.2047736
Median Absolute Deviation (MAD)1
Skewness-0.15506317
Sum416236
Variance1.768722
MonotonicityNot monotonic
2023-10-31T13:24:14.039687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 30563
23.5%
5 27957
21.5%
3 27794
21.4%
2 27383
21.1%
1 16051
12.4%
0 132
 
0.1%
ValueCountFrequency (%)
0 132
 
0.1%
1 16051
12.4%
2 27383
21.1%
3 27794
21.4%
4 30563
23.5%
5 27957
21.5%
ValueCountFrequency (%)
5 27957
21.5%
4 30563
23.5%
3 27794
21.4%
2 27383
21.1%
1 16051
12.4%
0 132
 
0.1%

Online boarding
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2526332
Minimum0
Maximum5
Zeros3080
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-10-31T13:24:14.112664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3507188
Coefficient of variation (CV)0.41526932
Kurtosis-0.69865492
Mean3.2526332
Median Absolute Deviation (MAD)1
Skewness-0.45691105
Sum422452
Variance1.8244412
MonotonicityNot monotonic
2023-10-31T13:24:14.185407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 38468
29.6%
3 27117
20.9%
5 26020
20.0%
2 21934
16.9%
1 13261
 
10.2%
0 3080
 
2.4%
ValueCountFrequency (%)
0 3080
 
2.4%
1 13261
 
10.2%
2 21934
16.9%
3 27117
20.9%
4 38468
29.6%
5 26020
20.0%
ValueCountFrequency (%)
5 26020
20.0%
4 38468
29.6%
3 27117
20.9%
2 21934
16.9%
1 13261
 
10.2%
0 3080
 
2.4%

Seat comfort
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4413613
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-10-31T13:24:14.254410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.3192889
Coefficient of variation (CV)0.38336251
Kurtosis-0.92283879
Mean3.4413613
Median Absolute Deviation (MAD)1
Skewness-0.48581778
Sum446964
Variance1.7405232
MonotonicityNot monotonic
2023-10-31T13:24:14.327493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 39756
30.6%
5 33158
25.5%
3 23328
18.0%
2 18529
14.3%
1 15108
 
11.6%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 15108
 
11.6%
2 18529
14.3%
3 23328
18.0%
4 39756
30.6%
5 33158
25.5%
ValueCountFrequency (%)
5 33158
25.5%
4 39756
30.6%
3 23328
18.0%
2 18529
14.3%
1 15108
 
11.6%
0 1
 
< 0.1%

Inflight entertainment
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3580767
Minimum0
Maximum5
Zeros18
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-10-31T13:24:14.396291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.334049
Coefficient of variation (CV)0.39726579
Kurtosis-1.0611127
Mean3.3580767
Median Absolute Deviation (MAD)1
Skewness-0.36638524
Sum436147
Variance1.7796867
MonotonicityNot monotonic
2023-10-31T13:24:14.470544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 36791
28.3%
5 31544
24.3%
3 23884
18.4%
2 21968
16.9%
1 15675
12.1%
0 18
 
< 0.1%
ValueCountFrequency (%)
0 18
 
< 0.1%
1 15675
12.1%
2 21968
16.9%
3 23884
18.4%
4 36791
28.3%
5 31544
24.3%
ValueCountFrequency (%)
5 31544
24.3%
4 36791
28.3%
3 23884
18.4%
2 21968
16.9%
1 15675
12.1%
0 18
 
< 0.1%

On-board service
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3830228
Minimum0
Maximum5
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-10-31T13:24:14.542541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2870994
Coefficient of variation (CV)0.38045837
Kurtosis-0.88884192
Mean3.3830228
Median Absolute Deviation (MAD)1
Skewness-0.42131965
Sum439387
Variance1.6566247
MonotonicityNot monotonic
2023-10-31T13:24:14.616452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 38703
29.8%
5 29492
22.7%
3 28542
22.0%
2 18351
14.1%
1 14787
 
11.4%
0 5
 
< 0.1%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 14787
 
11.4%
2 18351
14.1%
3 28542
22.0%
4 38703
29.8%
5 29492
22.7%
ValueCountFrequency (%)
5 29492
22.7%
4 38703
29.8%
3 28542
22.0%
2 18351
14.1%
1 14787
 
11.4%
0 5
 
< 0.1%

Leg room service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3508777
Minimum0
Maximum5
Zeros598
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-10-31T13:24:14.684989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3162518
Coefficient of variation (CV)0.39280806
Kurtosis-0.98301449
Mean3.3508777
Median Absolute Deviation (MAD)1
Skewness-0.34841439
Sum435212
Variance1.7325187
MonotonicityNot monotonic
2023-10-31T13:24:14.759518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 35886
27.6%
5 30905
23.8%
3 25056
19.3%
2 24540
18.9%
1 12895
 
9.9%
0 598
 
0.5%
ValueCountFrequency (%)
0 598
 
0.5%
1 12895
 
9.9%
2 24540
18.9%
3 25056
19.3%
4 35886
27.6%
5 30905
23.8%
ValueCountFrequency (%)
5 30905
23.8%
4 35886
27.6%
3 25056
19.3%
2 24540
18.9%
1 12895
 
9.9%
0 598
 
0.5%

Baggage handling
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1014.8 KiB
4
46761 
5
33878 
3
25851 
2
14362 
1
9028 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129880
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row4
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 46761
36.0%
5 33878
26.1%
3 25851
19.9%
2 14362
 
11.1%
1 9028
 
7.0%

Length

2023-10-31T13:24:14.842196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T13:24:14.936807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4 46761
36.0%
5 33878
26.1%
3 25851
19.9%
2 14362
 
11.1%
1 9028
 
7.0%

Most occurring characters

ValueCountFrequency (%)
4 46761
36.0%
5 33878
26.1%
3 25851
19.9%
2 14362
 
11.1%
1 9028
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 129880
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 46761
36.0%
5 33878
26.1%
3 25851
19.9%
2 14362
 
11.1%
1 9028
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
Common 129880
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 46761
36.0%
5 33878
26.1%
3 25851
19.9%
2 14362
 
11.1%
1 9028
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 46761
36.0%
5 33878
26.1%
3 25851
19.9%
2 14362
 
11.1%
1 9028
 
7.0%

Checkin service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3062673
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-10-31T13:24:15.013297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2661853
Coefficient of variation (CV)0.3829652
Kurtosis-0.82990408
Mean3.3062673
Median Absolute Deviation (MAD)1
Skewness-0.36656856
Sum429418
Variance1.6032253
MonotonicityNot monotonic
2023-10-31T13:24:15.089315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 36333
28.0%
3 35453
27.3%
5 25883
19.9%
1 16108
12.4%
2 16102
12.4%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 16108
12.4%
2 16102
12.4%
3 35453
27.3%
4 36333
28.0%
5 25883
19.9%
ValueCountFrequency (%)
5 25883
19.9%
4 36333
28.0%
3 35453
27.3%
2 16102
12.4%
1 16108
12.4%
0 1
 
< 0.1%

Inflight service
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6421928
Minimum0
Maximum5
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-10-31T13:24:15.165009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1766691
Coefficient of variation (CV)0.32306611
Kurtosis-0.35825341
Mean3.6421928
Median Absolute Deviation (MAD)1
Skewness-0.69157985
Sum473048
Variance1.3845501
MonotonicityNot monotonic
2023-10-31T13:24:15.238007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 47323
36.4%
5 34066
26.2%
3 25316
19.5%
2 14308
 
11.0%
1 8862
 
6.8%
0 5
 
< 0.1%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 8862
 
6.8%
2 14308
 
11.0%
3 25316
19.5%
4 47323
36.4%
5 34066
26.2%
ValueCountFrequency (%)
5 34066
26.2%
4 47323
36.4%
3 25316
19.5%
2 14308
 
11.0%
1 8862
 
6.8%
0 5
 
< 0.1%

Cleanliness
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2863258
Minimum0
Maximum5
Zeros14
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-10-31T13:24:15.307184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3136822
Coefficient of variation (CV)0.39974193
Kurtosis-1.0148014
Mean3.2863258
Median Absolute Deviation (MAD)1
Skewness-0.30092642
Sum426828
Variance1.725761
MonotonicityNot monotonic
2023-10-31T13:24:15.383777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 33969
26.2%
3 30639
23.6%
5 28416
21.9%
2 20113
15.5%
1 16729
12.9%
0 14
 
< 0.1%
ValueCountFrequency (%)
0 14
 
< 0.1%
1 16729
12.9%
2 20113
15.5%
3 30639
23.6%
4 33969
26.2%
5 28416
21.9%
ValueCountFrequency (%)
5 28416
21.9%
4 33969
26.2%
3 30639
23.6%
2 20113
15.5%
1 16729
12.9%
0 14
 
< 0.1%

Departure Delay in Minutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct466
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.713713
Minimum0
Maximum1592
Zeros73356
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-10-31T13:24:15.477446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile77
Maximum1592
Range1592
Interquartile range (IQR)12

Descriptive statistics

Standard deviation38.071126
Coefficient of variation (CV)2.5874589
Kurtosis100.64455
Mean14.713713
Median Absolute Deviation (MAD)0
Skewness6.8219803
Sum1911017
Variance1449.4107
MonotonicityNot monotonic
2023-10-31T13:24:15.579038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73356
56.5%
1 3682
 
2.8%
2 2855
 
2.2%
3 2535
 
2.0%
4 2309
 
1.8%
5 2136
 
1.6%
6 1884
 
1.5%
7 1748
 
1.3%
8 1618
 
1.2%
9 1552
 
1.2%
Other values (456) 36205
27.9%
ValueCountFrequency (%)
0 73356
56.5%
1 3682
 
2.8%
2 2855
 
2.2%
3 2535
 
2.0%
4 2309
 
1.8%
5 2136
 
1.6%
6 1884
 
1.5%
7 1748
 
1.3%
8 1618
 
1.2%
9 1552
 
1.2%
ValueCountFrequency (%)
1592 1
< 0.1%
1305 1
< 0.1%
1128 1
< 0.1%
1017 1
< 0.1%
978 1
< 0.1%
951 1
< 0.1%
933 1
< 0.1%
930 1
< 0.1%
921 1
< 0.1%
859 1
< 0.1%

Arrival Delay in Minutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct472
Distinct (%)0.4%
Missing393
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean15.091129
Minimum0
Maximum1584
Zeros72753
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-10-31T13:24:15.840452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313
95-th percentile78
Maximum1584
Range1584
Interquartile range (IQR)13

Descriptive statistics

Standard deviation38.46565
Coefficient of variation (CV)2.5488915
Kurtosis95.117114
Mean15.091129
Median Absolute Deviation (MAD)0
Skewness6.6701246
Sum1954105
Variance1479.6062
MonotonicityNot monotonic
2023-10-31T13:24:15.941760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 72753
56.0%
1 2747
 
2.1%
2 2587
 
2.0%
3 2442
 
1.9%
4 2373
 
1.8%
5 2083
 
1.6%
6 2021
 
1.6%
7 1794
 
1.4%
8 1751
 
1.3%
9 1566
 
1.2%
Other values (462) 37370
28.8%
ValueCountFrequency (%)
0 72753
56.0%
1 2747
 
2.1%
2 2587
 
2.0%
3 2442
 
1.9%
4 2373
 
1.8%
5 2083
 
1.6%
6 2021
 
1.6%
7 1794
 
1.4%
8 1751
 
1.3%
9 1566
 
1.2%
ValueCountFrequency (%)
1584 1
< 0.1%
1280 1
< 0.1%
1115 1
< 0.1%
1011 1
< 0.1%
970 1
< 0.1%
952 1
< 0.1%
940 1
< 0.1%
924 1
< 0.1%
920 1
< 0.1%
860 1
< 0.1%

Satisfaction
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1014.8 KiB
neutral or dissatisfied
73452 
satisfied
56428 

Length

Max length23
Median length23
Mean length16.917524
Min length9

Characters and Unicode

Total characters2197248
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowneutral or dissatisfied
2nd rowneutral or dissatisfied
3rd rowsatisfied
4th rowneutral or dissatisfied
5th rowsatisfied

Common Values

ValueCountFrequency (%)
neutral or dissatisfied 73452
56.6%
satisfied 56428
43.4%

Length

2023-10-31T13:24:16.047280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T13:24:16.138603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
neutral 73452
26.5%
or 73452
26.5%
dissatisfied 73452
26.5%
satisfied 56428
20.4%

Most occurring characters

ValueCountFrequency (%)
i 333212
15.2%
s 333212
15.2%
e 203332
9.3%
t 203332
9.3%
a 203332
9.3%
d 203332
9.3%
r 146904
6.7%
146904
6.7%
f 129880
 
5.9%
n 73452
 
3.3%
Other values (3) 220356
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2050344
93.3%
Space Separator 146904
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 333212
16.3%
s 333212
16.3%
e 203332
9.9%
t 203332
9.9%
a 203332
9.9%
d 203332
9.9%
r 146904
7.2%
f 129880
 
6.3%
n 73452
 
3.6%
u 73452
 
3.6%
Other values (2) 146904
7.2%
Space Separator
ValueCountFrequency (%)
146904
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2050344
93.3%
Common 146904
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 333212
16.3%
s 333212
16.3%
e 203332
9.9%
t 203332
9.9%
a 203332
9.9%
d 203332
9.9%
r 146904
7.2%
f 129880
 
6.3%
n 73452
 
3.6%
u 73452
 
3.6%
Other values (2) 146904
7.2%
Common
ValueCountFrequency (%)
146904
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2197248
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 333212
15.2%
s 333212
15.2%
e 203332
9.3%
t 203332
9.3%
a 203332
9.3%
d 203332
9.3%
r 146904
6.7%
146904
6.7%
f 129880
 
5.9%
n 73452
 
3.3%
Other values (3) 220356
10.0%

Interactions

2023-10-31T13:24:09.121372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:39.068520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:40.941072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:42.722300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:44.466041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:46.356739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:48.090476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:49.798978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:51.487745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:53.357794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:55.057620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:56.756607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:58.596160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:00.316820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:02.033670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:03.735293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:05.583635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:07.285754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:09.224713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:39.182998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:41.041552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:42.822112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:44.562256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:46.450886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:48.184962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:49.894716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:51.590101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:53.453142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:55.153701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:56.853725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:58.690948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:00.411078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:02.129278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:03.833620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:05.683832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:07.390310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:09.331092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:39.288766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:41.142124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:42.925327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:44.665416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:46.553140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:48.285199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:49.996121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:51.687093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:53.550379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:55.257793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:56.950932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:58.788036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:00.513576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:02.227251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:03.931740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:05.781244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:07.494463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:09.428094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:39.385184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:41.243010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:43.018745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:44.760086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:46.648050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:48.376687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:50.086294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:51.916586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:53.641862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:55.347734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:57.045380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:58.880378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:00.607271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:02.320332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:04.027893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:05.876398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:07.600858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:09.526824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:39.481116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:41.339557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:43.116227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:44.869288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:46.750267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:48.467875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:50.178024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:52.008822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:53.735462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:55.438871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:57.137486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:58.974733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:00.702917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:02.410444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:04.121534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:05.966937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:07.702873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:09.632965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:39.665684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:41.435876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:43.215401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:44.964868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:46.846500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:48.560569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:50.271630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:52.104355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:53.829111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:55.533584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:57.231228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:59.071141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:00.796771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:02.505795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:04.214644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:06.062616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:07.805007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:09.739999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:39.759369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:41.532790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:43.308279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:45.060246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:46.946548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:48.648126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:50.362594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:52.201150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:53.919880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:55.626287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:57.325594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:59.163634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:00.890904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:02.600857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:04.308372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:06.153737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:07.906282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:09.850198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:39.855406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:41.626674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:43.402726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:45.152816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:47.038391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:48.742200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:50.453665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:52.292264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:54.013728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:55.719938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:57.416600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:59.258257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:00.982622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:02.693859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:04.399847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:06.245800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:08.004424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:09.950456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:39.950680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:41.722674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:43.494310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:45.244498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:47.129744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:48.836369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:50.543287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:52.383119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:54.107987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:55.809029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:57.510301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:59.357511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:01.073329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:02.787415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:04.495025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:06.337628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:08.105239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:10.050668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:40.048523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:41.818877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:43.590013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:45.341336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:47.227572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:48.932345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:50.634807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:52.482649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:54.200136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:55.900141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:57.601914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:59.447068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:01.166383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:02.879827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:04.586639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:06.429370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:08.206545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:10.148785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:40.142233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:41.915994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:43.683880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:45.578021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:47.317459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:49.025767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:50.724725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:52.577094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:54.291603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:55.992504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:57.692452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:59.540870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:01.263913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:02.972379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:04.680351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:06.522571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:08.307855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:10.250649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:40.239836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:42.011977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:43.777954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:45.668642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:47.413691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:49.122349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:50.816873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:52.680057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:54.388272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:56.084041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:57.785294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:59.634793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:01.360503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:03.063621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:04.910376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:06.613301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:08.407667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:10.352540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:40.337148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:42.111927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:43.874857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:45.761992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:47.508317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:49.214420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:50.911059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:52.772102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:54.478967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:56.176364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:57.876180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:59.725725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:01.455001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:03.157304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:04.999937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:06.705580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:08.508841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:10.454138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:40.435311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:42.208279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:43.970302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:45.855204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:47.601618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:49.306943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:51.003110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:52.862769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:54.570067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:56.269083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:57.970893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:59.821311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:01.548456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:03.248559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:05.092178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:06.801033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:08.605752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:10.553123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:40.532184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:42.311175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:44.065436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:45.948630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:47.694669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:49.402905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:51.098406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:52.956522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:54.664157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:56.362548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:58.063180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:59.917313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:01.639485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:03.341207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:05.182781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:06.891364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:08.706596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:10.655077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:40.630316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:42.405801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:44.157331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:46.054307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:47.789166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:49.495000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:51.189501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:53.050061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:54.753086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:56.456672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:58.158470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:00.010189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:01.733304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:03.436266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:05.274316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:06.983228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:08.805604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:10.752908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:40.725885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:42.501137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:44.253560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:46.146734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:47.882043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:49.589546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:51.284428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:53.144498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:54.846011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:56.549763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:58.384250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:00.104658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:01.826704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:03.529811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:05.369710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:07.076796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:08.904269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:10.862005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:40.835854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:42.611933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:44.361856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:46.250990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:47.985069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:49.693610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:51.387904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:53.251821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:54.952199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:56.654385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:23:58.494379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:00.212238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:01.929466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:03.634795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:05.476446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:07.183204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-31T13:24:09.010386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-31T13:24:16.225114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
idAgeFlight DistanceInflight wifi serviceDeparture/Arrival time convenientEase of Online bookingGate locationFood and drinkOnline boardingSeat comfortInflight entertainmentOn-board serviceLeg room serviceCheckin serviceInflight serviceCleanlinessDeparture Delay in MinutesArrival Delay in MinutesGenderCustomer TypeType of TravelClassBaggage handlingSatisfaction
id1.0000.0230.134-0.024-0.0030.012-0.000-0.0020.0560.0540.0010.0520.0410.0750.0740.0220.057-0.0040.0030.0110.0200.1290.0650.024
Age0.0231.0000.0730.0150.0350.020-0.0010.0210.2140.1600.0800.0700.0500.038-0.0330.053-0.009-0.0110.0150.3780.3420.2080.0600.282
Flight Distance0.1340.0731.0000.006-0.0130.0640.0020.0460.1940.1370.1070.1020.1180.0720.0620.0820.025-0.0020.0080.2490.2810.3440.0380.312
Inflight wifi service-0.0240.0150.0061.0000.3400.7110.3350.1300.4360.1180.1980.1150.1500.0430.1050.129-0.029-0.0360.0040.0370.1830.1000.1210.526
Departure/Arrival time convenient-0.0030.035-0.0130.3401.0000.4400.453-0.0010.0630.009-0.0130.0700.0050.0970.0890.009-0.004-0.0050.0090.2940.2870.0980.0710.068
Ease of Online booking0.0120.0200.0640.7110.4401.0000.4630.0280.3680.0250.0420.0380.0960.0090.0350.014-0.010-0.0130.0050.0550.1880.1160.0330.314
Gate location-0.000-0.0010.0020.3350.4530.4631.000-0.003-0.0010.0010.002-0.029-0.005-0.040-0.009-0.0060.0060.0070.0100.1250.1540.1110.0550.154
Food and drink-0.0020.0210.0460.130-0.0010.028-0.0031.0000.2400.5580.6110.0570.0320.0820.0450.647-0.022-0.0310.0090.0770.0820.0800.0370.225
Online boarding0.0560.2140.1940.4360.0630.368-0.0010.2401.0000.4400.3010.1750.1380.2170.1080.344-0.033-0.0490.0480.1960.2360.2480.0930.618
Seat comfort0.0540.1600.1370.1180.0090.0250.0010.5580.4401.0000.6050.1460.1180.1970.0980.668-0.020-0.0370.0390.1700.1360.1750.0820.388
Inflight entertainment0.0010.0800.1070.198-0.0130.0420.0020.6110.3010.6051.0000.4360.3140.1200.4240.681-0.029-0.0440.0000.1160.1680.1530.3530.421
On-board service0.0520.0700.1020.1150.0700.038-0.0290.0570.1750.1460.4361.0000.3660.2360.5690.124-0.028-0.0480.0210.0750.0870.1610.4000.332
Leg room service0.0410.0500.1180.1500.0050.096-0.0050.0320.1380.1180.3140.3661.0000.1450.3740.097-0.006-0.0200.0540.0760.1720.1640.2680.342
Checkin service0.0750.0380.0720.0430.0970.009-0.0400.0820.2170.1970.1200.2360.1451.0000.2500.172-0.018-0.0340.0100.0320.0180.1270.1420.250
Inflight service0.074-0.0330.0620.1050.0890.035-0.0090.0450.1080.0980.4240.5690.3740.2501.0000.102-0.035-0.0570.0460.0550.0420.1320.4930.282
Cleanliness0.0220.0530.0820.1290.0090.014-0.0060.6470.3440.6680.6810.1240.0970.1720.1021.000-0.018-0.0310.0180.1020.0970.1180.0640.316
Departure Delay in Minutes0.057-0.0090.025-0.029-0.004-0.0100.006-0.022-0.033-0.020-0.029-0.028-0.006-0.018-0.035-0.0181.0000.7400.0020.0000.0040.0000.0060.018
Arrival Delay in Minutes-0.004-0.011-0.002-0.036-0.005-0.0130.007-0.031-0.049-0.037-0.044-0.048-0.020-0.034-0.057-0.0310.7401.0000.0000.0000.0000.0000.0060.018
Gender0.0030.0150.0080.0040.0090.0050.0100.0090.0480.0390.0000.0210.0540.0100.0460.0180.0020.0001.0000.0310.0090.0120.0470.011
Customer Type0.0110.3780.2490.0370.2940.0550.1250.0770.1960.1700.1160.0750.0760.0320.0550.1020.0000.0000.0311.0000.3080.1230.0650.186
Type of Travel0.0200.3420.2810.1830.2870.1880.1540.0820.2360.1360.1680.0870.1720.0180.0420.0970.0040.0000.0090.3081.0000.5540.0490.450
Class0.1290.2080.3440.1000.0980.1160.1110.0800.2480.1750.1530.1610.1640.1270.1320.1180.0000.0000.0120.1230.5541.0000.1370.503
Baggage handling0.0650.0600.0380.1210.0710.0330.0550.0370.0930.0820.3530.4000.2680.1420.4930.0640.0060.0060.0470.0650.0490.1371.0000.289
Satisfaction0.0240.2820.3120.5260.0680.3140.1540.2250.6180.3880.4210.3320.3420.2500.2820.3160.0180.0180.0110.1860.4500.5030.2891.000

Missing values

2023-10-31T13:24:11.220250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-31T13:24:11.641069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idGenderCustomer TypeAgeType of TravelClassFlight DistanceInflight wifi serviceDeparture/Arrival time convenientEase of Online bookingGate locationFood and drinkOnline boardingSeat comfortInflight entertainmentOn-board serviceLeg room serviceBaggage handlingCheckin serviceInflight serviceCleanlinessDeparture Delay in MinutesArrival Delay in MinutesSatisfaction
070172MaleLoyal Customer13Personal TravelEco Plus460343153554344552518.0neutral or dissatisfied
15047Maledisloyal Customer25Business travelBusiness2353233131115314116.0neutral or dissatisfied
2110028FemaleLoyal Customer26Business travelBusiness11422222555543444500.0satisfied
324026FemaleLoyal Customer25Business travelBusiness56225552222253142119.0neutral or dissatisfied
4119299MaleLoyal Customer61Business travelBusiness2143333455334433300.0satisfied
5111157FemaleLoyal Customer26Personal TravelEco11803421121134444100.0neutral or dissatisfied
682113MaleLoyal Customer47Personal TravelEco127624232222334352923.0neutral or dissatisfied
796462FemaleLoyal Customer52Business travelBusiness20354344555555545440.0satisfied
879485FemaleLoyal Customer41Business travelBusiness8531222433112141200.0neutral or dissatisfied
965725Maledisloyal Customer20Business travelEco10613334233223443200.0neutral or dissatisfied
idGenderCustomer TypeAgeType of TravelClassFlight DistanceInflight wifi serviceDeparture/Arrival time convenientEase of Online bookingGate locationFood and drinkOnline boardingSeat comfortInflight entertainmentOn-board serviceLeg room serviceBaggage handlingCheckin serviceInflight serviceCleanlinessDeparture Delay in MinutesArrival Delay in MinutesSatisfaction
12987030263Maledisloyal Customer42Business travelEco102444423433312233017.0neutral or dissatisfied
12987190347Femaledisloyal Customer39Business travelBusiness4041113212253444200.0neutral or dissatisfied
12987286816MaleLoyal Customer41Business travelEco69222222222233232153.0neutral or dissatisfied
129873120654MaleLoyal Customer52Business travelBusiness2803333344444434300.0satisfied
12987425309Femaledisloyal Customer36Business travelEco4321513414452523400.0neutral or dissatisfied
12987578463Maledisloyal Customer34Business travelBusiness5263331434432445400.0neutral or dissatisfied
12987671167MaleLoyal Customer23Business travelBusiness6464444444445555400.0satisfied
12987737675FemaleLoyal Customer17Personal TravelEco8282515212243454200.0neutral or dissatisfied
12987890086MaleLoyal Customer14Business travelBusiness11273333444432545400.0satisfied
12987934799FemaleLoyal Customer42Personal TravelEco2642525422112111100.0neutral or dissatisfied